Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence.

ABSTRACT: Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses; however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.973/0.951 in China and 0.730/0.942 in the United Kingdom), achieving compar

SUBMITTER: Bai X 

PROVIDER: S-EPMC10805468 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature